The low-rank hurdle model
Machine Learning
2017-09-07 v1 Machine Learning
Abstract
A composite loss framework is proposed for low-rank modeling of data consisting of interesting and common values, such as excess zeros or missing values. The methodology is motivated by the generalized low-rank framework and the hurdle method which is commonly used to analyze zero-inflated counts. The model is demonstrated on a manufacturing data set and applied to the problem of missing value imputation.
Cite
@article{arxiv.1709.01860,
title = {The low-rank hurdle model},
author = {Christopher Dienes},
journal= {arXiv preprint arXiv:1709.01860},
year = {2017}
}
Comments
14 pages, 2 figures, 2 tables